A data-driven approach for the prediction and optimization of an industrial scale chemical vapour deposition process
Speaker: Paris Papavsileiou (Faculty of Science, Technology and Medicine; University of Luxembourg)
Title: A data-driven approach for the prediction and optimization of an industrial scale chemical vapour deposition process
Time: Wednesday, 2021.10.20, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Chemical vapour deposition (CVD) processes are famous for their complexity. That becomes evident when all the transport mechanisms and chemical reactions that take place inside a CVD reactor are considered. The implementation of a Computational Fluid Dynamics (CFD) model for the simulation of such processes is possible, however the aforementioned complexity usually leads to high computational costs. For this reason, a purely data-driven approach is investigated. Several supervised learning algorithms are tested and their performance on the dataset is compared. This approach allows for efficient and accurate predictions, for entire reactor geometries and different set-ups at a fraction of the time that even a simplified CFD model would require.